“Deep learning” is defined broadly enough that I’m not sure it can be replaced.

Deep learning just means learning several steps of a processing pipeline, instead of learning only one step. From this point of view, there is a combinatorial explosion in the number of deep algorithms relative to the number of shallow algorithms.

In 2006–2011, “deep learning” was popular, but “deep learning” mostly meant stacking unsupervised learning algorithms on top of each other in order to define complicated features for supervised learning.

Since 2012, “deep learning” has mostly meant using back-propagation to optimize all the parameters in a deep computational graph representing some differentiable function.

It seems very likely that we’ll soon have algorithms that are more Bayesian (rather than based on a single point estimate of the best parameters), that use more non-differentiable operations, etc. We will still probably think of them as “deep” though, when we stop to think about whether they are “deep” or not.

I also think we’ll quit thinking much about the distinction between “deep learning” and other kinds of learning. Deep learning is already so common that it’s not very exotic anymore. It doesn’t require special branding and PR efforts to get it accepted.

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